Hydrological Data Driven Modelling

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This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.Technical Report. http://pyml.sourceforge.net/doc/howto.pdf Bishop CM (1996)
Neural networks for pattern recognition. Oxford University Press. ISBN 0-19-
853864-2 Boser BE, Guyon I, Vapnik V (1992) A training algorithm for optimal
margin classifiers. In: Proceedings fifth ACM workshop on computational learning
theory, pp 144a152 Box GE, Jenkins G (1970) Time ... Holden-Day, San
Francisco (2nd edn 1976) Box GEP, Jenkins GM (1976) Time series analysis:
Forecasting andanbsp;...

Title

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Hydrological Data Driven Modelling

Author

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Renji Remesan, Jimson Mathew

Publisher

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Springer - 2014-11-03

ISBN-13

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